MS&E323 - Stochastic Simulation

Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.
Career
Graduate
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No

Course Component
Discussion
Enrollment Optional?
Yes
Course Component
Lecture
Enrollment Optional?
No